Unified Etching and Protection of Faceted Silver Nanostructures by DNA Oligonucleotides
Bibliographic record
Abstract
Single-stranded DNA oligonucleotides have been widely used to functionalize metal nanoparticles, and the nanoparticles are often assumed to be stable. We herein communicate that DNA can both protect and etch silver nanomaterials, such as triangular plates (AgTPs). For DNA moieties with a high affinity to Ag such as polycytosine (poly-C) and polyguanine (poly-G), they display concentration-dependent etching, and the DNA length was vital. Etching was less effective when DNA is folded into more compact structures. Polythymine (poly-T) DNA is adsorbed very weakly on silver and it cannot etch the AgTPs. Instead, poly-T DNA effectively protects AgTPs from oxidation or dissolution against various etching conditions including Br–, Hg²⁺, H₂O₂, and heat. Compared to other types of synthetic polymers, poly-T DNA shows a much stronger protection effect. The change from protection to etching can be rationalized based on the interaction strength with silver. Adsorbed DNA can protect the AgTPs, while a high concentration of strongly binding DNA can increase its solubility and appear to give an etching effect. This understanding is critical for rational design of biosensors and controlled growth of nanomaterials.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".